Fault detection in rotor driven equipment using rotational invariant transform of   sub-sampled 3-axis vibrational data

ABSTRACT

A method and system of detecting faults in rotor driven equipment includes generating data from one or more vibration sensors communicatively coupled to the rotor driven equipment. The data from the one or more machine wearable sensors is collected onto a mobile data collector. The data is sampled at random to estimate a maximum value. Further, a sampling error may be controlled under a predefined value. The data may be analyzed through a combination of Cartesian to Spherical transformation, statistics of the entity extraction (such as variance of azimuthal angle), big data analytics engine and a machine learning engine. A fault is displayed on a user interface associated with the rotor driven equipment.

FIELD OF TECHNOLOGY

The present invention generally relates to fault detection in rotorequipment. More specifically it relates to fault detection in rotordriven equipment based on a combination of low frequency vibration data,spherical transformation, machine learning and big data architecture.

BACKGROUND

Internet of Things (IoT) is a network of uniquely-identifiable, purposed“Things” that are enabled to communicate data pertaining thereto over awide communication network, whereby the communicated data form a basisfor manipulating the operation of the “Things”. The “Thing” in theInternet of Things could virtually be anything that fits into a commonpurpose thereof. For example, the “Thing” could be a person with a heartrate monitor implant, a farm animal with a biochip transponder, anautomobile that has built-in sensors to alert its driver when tirepressure is low, or the like, or any other natural or man-made entitythat can be assigned a unique IP address and provided with the abilityto transfer data over a communication network. Notably, if all theentities in an IoT are machines, then the IoT is referred to as a“Machine to Machine” (M2M) IoT or simply, as M2M IoT.

It is apparent from the aforementioned examples that an entity becomes a“Thing” of an M2M IoT especially, when the entity is attached with oneor more sensors capable of capturing one or more types of datapertaining thereto: segregating the data (if applicable); selectivelycommunicating each segregation of data to one or more fellow “Things”;receiving one or more control commands (or instructions) from one ormore fellow “Things”, wherein the control commands are based on the datareceived by the fellow “Things”; and executing the control commandsresults in the manipulation or management of the operation of thecorresponding entity. Therefore, in an IoT-enabled system, the “Things”basically manage themselves without any human intervention, thusdrastically improving the efficiency thereof.

Some of the prior art non-patent literature have discussed gearcondition monitoring based on vibration analysis techniques. Thedetection and diagnostic capability of some of the most electivetechniques were discussed and compared on the basis of experimentalresults concerning a gear pair affected by a fatigue crack. Inparticular, the results of approaches based on time-frequency andcyclostationarity analysis were compared against those obtained by meansof the well-accepted cepstrum analysis and time-synchronous averageanalysis.

In prior art non patent literature, the sensitivity and robustness ofthe currently known techniques such as phase and amplitude demodulation,beta kurtosis and wavelet transform were discussed. Fourgear test caseswere used: healthy gears, cracked, led and chipped gears. Othernon-patent literature also discussed SVM (Support Vector machine)classification performance in rolling bearing diagnosis. SVM wereintroduced into rolling bearings intelligent fault diagnosis due to thefact that it was hard to obtain enough fault samples in practice and dueto the perfect performance of SVM.

Vibration and wear debris analyses were the two main conditionmonitoring techniques for machinery maintenance and fault diagnosis. Inpractice, these two techniques were usually conducted independently andcould only diagnose about 30-40% of faults when used separately. Pastnon-patent literature attempted to combine these two techniques toprovide greater and more reliable information.

Rolling element bearings comes under the critical category in manyrotating machineries, mainly in chemical industry, aviation industry,nuclear power stations etc. Vibration monitoring and analysis may beuseful tool in the field of predictive maintenance. Health of rollingelement bearings was previously attempted to be identified usingvibration monitoring as vibration signature reveals importantinformation about the fault development within them.

Previous non-patent literature also discussed development of an expertsystem for vibration analysis of a fixed plant, as well as laboratoryand industry testing. The expert system incorporated triaxial anddemodulated frequency and time domain vibration data analysis algorithmsfor high accuracy fault detection.

Also, some non-patent literature suggested the use of wavelet packetanalysis for fault diagnosis of roller bearings. Further, prior artliterature also suggested the use of neural networks and wavelettransforms to diagnose faults in rotating machinery.

Some non-patent literature used information contained in vibrationsignals to devise a system for alarm detection and diagnosis of failuresin mechanical components of power wind mills. A method that uses theone-class-v-SVM paradigm was employed.

Further, wavelets were applied to gearbox vibration signals for faultdetection.

However, none of the prior arts show or suggest the use of low frequencyvibration data or use of big data architectures to diagnose faults inrotor driven equipment.

It is evident from the discussion of the aforementioned prior arts thatnone of them pave way for fault detection in rotor driven equipmentthrough big data analytics. Therefore, there exists a need in the artfor a solution to the aforementioned problem.

SUMMARY OF THE INVENTION

Disclosed are a method, an apparatus and/or a system of fault detectionin rotor driven equipment through big data analytics.

In one aspect, the present invention relates to a method of detectingfaults in rotor driven equipment, the method includes generating datafrom one or more machine wearable sensors communicatively coupled to therotor driven equipment. The data from the one or more machine wearablesensors is collected onto a mobile data collector. The data is sampledat random to estimate a maximum value. Further, a sampling error may becontrolled under a predefined value. The data may be analyzed through acombination of big data analytics engine and a machine learning engine.A fault is displayed on a user interface associated with the rotordriven equipment.

In another aspect, the present invention relates to a method ofpredicting rotor driven equipment issues, the method comprises:collecting, through a processor, data associated with one or moremachine wearable sensors associated with a rotor driven equipment; andtransmitting the data collected at the one or more machine wearablesensors over a communication network to a mobile data collector. Thedata is collected over a finite time period and transmitted to a machinelearning engine. The machine learning engine is associated with acomputer database hosting real time and historical data. The methodfurther includes visualizing, through a processor, one or more rotordriven equipment issue based on an analysis through a combination of abig data engine and a machine learning engine. Further, the methodincludes indicating the one or more rotor driven equipment issuesthrough a user interface dynamic and setting an alarm, through aprocessor, for the one or more rotor driven equipment issue.

The methods and systems disclosed herein may be implemented in any meansfor achieving various aspects of intended results and may be executed ina form of a machine-readable medium embodying a set of instructionsthat, when executed by a machine, cause the machine to perform any ofthe operations disclosed herein. Other features will be apparent fromthe accompanying drawings and from the detailed description thatfollows.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the present invention are illustrated by way ofexample and not as limitation in the figures in which similar elementsare indicated with same references.

FIG. 1 is a diagrammatic representation of an overall architecture faultdetection in rotor driven equipment using low frequency vibration dataand big data architecture, according to one embodiment.

FIG. 2 is a diagrammatic representation of a data processing systemcapable of processing a set of instructions to perform any one or moreof the methodologies herein, according to one embodiment.

FIG. 3 is a process flow diagram detailing the operations of a method ofpredicting an electrical line issue, according to one or moreembodiments.

FIG. 4 is a diagrammatic representation of a circular gauge to depict astate of a rotor driven equipment, according to one example embodiment.

FIG. 5 shows a data flow pipeline associated with machine learning,according to one embodiment.

FIG. 6 is a graph showing standard error of estimate of kurtosis againsta sample size, according to one or more embodiments.

FIG. 7 shows a table wherein the sample size depends on Standard Errorand different percentiles, according to one embodiment.

FIG. 8 depicts V's of big data: volume, velocity and variety, accordingto one or more embodiments.

FIG. 9 is a diagrammatic representation of a predictive maintenancecircular gauge associated with a rotor driven equipment, according toone embodiment.

FIG. 10 is a flow diagram detailing data from a three axis vibrationaldata to a fuel gauge based predictive maintenance, according to oneembodiment.

Other features of the present embodiments will be apparent from theaccompanying drawings and from the detailed description that follows.

DETAILED DESCRIPTION

Example embodiments as described below may be used to provide a method,an apparatus and/or a system for fault detection in rotor drivenequipment through big data analytics. Although the present embodimentshave been described with reference to specific example embodiments, itwill be evident that various modifications and changes may be made tothese embodiments without departing from the broader spirit and scope ofthe various embodiments.

In one or more embodiments, vibrations of a fault free rotor in timedomain may be represented by

x_j(t)=Σ_(i=1) ^(∞)α_(i)*sin(i*Ω*t)|j=1,2,2

Ω is fundamental frequency and i, j, and k are three Cartesian vectors.In vibration analysis of a rotor, revolution per second of rotor may bemultiplied by 2π.

Fundamental harmonics and other harmonics generated by a rotor maydepend on the rotor's speed. Rotation speed of the rotor may commonlyvary between 200-3000 RPM (Revolutions Per Minute). With such rotationspeeds, the fundamental harmonics and other harmonics generated may bein high frequency range. Traditional methods of fault detection in rotormachinery are based on physics of harmonics in high frequency ranges.

Due to high frequency range solutions, traditional methods use expensivevibration sensors. The expensive vibration sensors may sample vibrationdata at very high sampling rate. Local electronics to performcomputations like real time fast Fourier transform may be used. The realtime fast Fourier transform may detect the difference between harmonicsin normal and faulty states.

Further, due to high frequency sampling, data may need to be transmittedat high data rate. The high data rate transmission may not be possibleover lower bandwidth wireless sensor network such as Zigbee and/orPiconet. High data rate is not suitable for real time analysis on aserver or locally. Real time analysis may require a higher computationalRAM (Random Access Memory) and processing power.

In one or more embodiments, random sampling may include a subset of astatistical population. Each member of the subset may have an equalprobability of being chosen. A simple random sample may be an unbiasedrepresentation of a group.

In one or more embodiments, techniques described herein do not involveenvelope detection but random sampling of a vibration signal envelope.The random sampling may be to estimate maximum value through apercentile and/or Root Meansquare value through variance of a signal.The random sampling may involve large number of samples to reducesampling errors due to finite size of a batch of data collected. Instatistics, sampling error may be caused by observing a sample insteadof a whole population. The sampling error may be the difference betweena sample statistic used to estimate a population parameter and an actualbut unknown value of the population parameter.

Techniques described herein may include variance, kurtosis, percentileand crest factor (maximum value/RMS value of a periodic signal).Estimate of a size of batch may be important to control sampling errorunder a predetermined value such as 1%.

In one or more embodiments, a distribution's degree of kurtosis may bedefined as

η=β₂−3,

wherein

${\beta_{2} = \frac{{\Sigma \left( {Y - \mu} \right)}^{4}}{n\; \sigma^{4}}},$

the expected value of the distribution of Z scores which have beenraised to the 4th power.

β2 may be often referred to as “Pearson's kurtosis,” and β2−3 (oftensymbolized with γ2) may be referred to as Kurtosis excess and/orFisher's kurtosis. An unbiased estimator for γ2 may be

$g_{2} = {\frac{{n\left( {n + 1} \right)}\Sigma \; Z^{4}}{\left( {n - 1} \right)\left( {n - 2} \right)\left( {n - 3} \right)} - \frac{3\left( {n - 1} \right)^{2}}{\left( {n - 2} \right)\left( {n - 3} \right)}}$

For large sample sizes (n>1000), g2 may be distributed approximatelynormally, with a standard error (S.E) of approximately: √{square rootover (24/n)}

So,S.E(g2)=√(24/n).

FIG. 6 is a graph showing standard error (S. E) of estimate of kurtosisagainst a sample size, according to one or more embodiments. Forexample, if the S. E reduces to 5% or lesser, then the sample size mustbe 9600 or greater.

In one or more embodiments, a root mean square (RMS), may also be knownas the quadratic mean. In statistics, RMS may be a statistical measuredefined as the square root of a mean of squares of a sample.

RMS=√Σ₁ ^(n)×_(i) ²

where x₁, x₂ . . . x_(n) may be Gaussian distribution with mean zero andvariance 1. Then standard error of RMS may be a function of n, where nmay be a sample size.

Standard Error may be:

${S.E.({RMS})} = \sqrt{1 - \frac{2*\left( {{Gamma}\left( \frac{n + 1}{2} \right)} \right)^{2}}{n*\left( {{Gamma}\left( \frac{n}{2} \right)} \right)^{2}}}$

where Gamma(x) (x−1)*Gamma(x−1)=(x−1)!

In an example embodiment, solving the above equation for S.E, it may beclaimed that 1% sampling error (S.E(RMS)) may be obtained at a samplesize of 4800. i.e., S.E(RMS)=0.01023, when n=4793. Therefore, if n=4800(approximately), S.E(RMS)=0.01.

In one or more embodiments, a percentile may be a measure used instatistics indicating a value, below which a given percentage ofobservations in a group of observations fall. For example, the 95thpercentile is a value (or score), below which 95 percent of observationsmay be found. Statistically if xp is the P-th percentile and if X is arandom variable following any distribution the

Probability(X<x _(p))=P

If x₁, x₂ . . . x_(n) are Gaussian distribution with mean zero andvariance one (1) then Standard error of x_(p) is

${S.{E\left( x_{p} \right)}} = {\frac{1}{f\left( x_{p} \right)}\sqrt{\frac{p\left( {1 - p} \right)}{n}}}$

where f(•) is a density function of N(0,1). Now S.E(x_(p))<0.01 implies

$n > \frac{2\; n\; {p\left( {1 - p} \right)}}{(e)^{2}{\exp^{2}\left( \frac{x_{p}^{2}}{2} \right)}}$

e is standard error, in this case it is 0.01.

To estimate x_(p) simulation method may been applied. The simulationmethod may give an estimation of x_(p) is as 1.63224 as the distributionis N(0,1).

FIG. 7 shows a table, wherein the sample size depends on Standard Errorand different percentiles, according to one embodiment. For example, toestimate 98th percentile with less than 2% standard error the samplesize may be greater than 4,420.

In one or more embodiments, a system of machine wearable sensors mountedon machines for collecting temperature, vibration, current, voltage,phase lag, vacuum, magnetic field and gyroscopic data may be used torecord data. The data collected may be in structured and/orun-structured format. For example, audio data may be in an un-structuredformat. Data may be collected with primary meta-data classification suchas “baseline” and “test” where baseline refers to normal operatingcondition and/or a condition referring to a good machine and test datamay be classified according to the need of the testing.

Sensor data may be fed via a data hub (example: a mobile application) toa cloud server. The cloud server may collect, analyze and store thesensor data using Big Data technology such as Kafka, NoSql, Cassandraand Apache Spark.

Machine learning may be a part of artificial intelligence. Intelligencein machines may be developed by developing algorithms that may learnfrom data over time and improve the predictions accordingly. Two mostwidely used machine learning methods are supervised and un-supervisedlearning methods. To analyze and predict the machine faults both thesupervised and un-supervised learning methods may be used.

FIG. 5 shows a data flow pipeline associated with machine learning,according to one embodiment. The data flow pipeline may be designed toaccommodate data volume and automation.

In an example embodiment, a user may need to identify whether a rotormachinery is operated abusive manner or not. In terms of machinelearning, it can be formulated as binary and/or multiclass supervisedclassification problem where the objective is to classify whether thepressure at which the machine is being operated is normal or abusive.

The data collected from different sensors may be directional and/ornon-directional data. For example, temperature related data may be in ascalar form but magnetic field and/or vibration data may be in a vectorform. Similar sensor packages may be installed across differentfactories over different machines. To develop a generalized solutionwhich may work across sensors installed at different locations indifferent conditions, data normalization and/or data transformation maybe performed.

The data collected by sensors may be in Cartesian coordinate system. Sothe raw vector data from the sensor is not rotationally invariant.Rotational invariance of data is compulsory for MEMS based accelerometersensor since its internal frame of axis rotates all the time. In orderto make it rotationally invariant spherical transformation may beperformed using the following equations:

$r = \sqrt{x^{2} + y^{2} + z^{2}}$ $\theta = {\cos^{- 1}\frac{z}{r}}$$\varphi = {\tan^{- 1}\frac{y}{x}}$

In one or more embodiments, ensuring data quality may be a pre-step toany data analysis. In data quality: completeness, validity, consistency,timeliness and accuracy of data may be tested. After data check,exploratory data analysis may be used performed in order to perform datacleaning and feature engineering. Through exploratory data analysis forapplications like Abusive operation detection, oil level detection, highbelt tension detection, oil state identification, etc., followingfeatures have been shortlisted to build machine learning based model.The following Table 1 shows the features and definitions along withrespective explanations.

TABLE 1 Feature Definition Explanation Variance of Theta variance ofenergy ratio of Indicative of unstable rotation principal axis tooblique axis which happens when rotor plane is becoming unstableKurtosis of Theta Statistical distribution of Indicative of loss ofbalance in energy distribution across the rotor axis Crest factor Peakto RMS, indicative of Indicative of bearing friction % of harmonics inthe signal RMS of R The root mean square value Total energy as movingaverage of the amplitude of vibration Kurtosis of R Nature statisticaldistribution Indicative of bearing failure of vibration Percentile ratioof RMS 98:2, 95:5, 90:10, 80:20 etc. Measure the normalized differencein vibration introduced by faults Ratio of RMS Ratio of RMS of X, Y; Y,Z Indicative of belt tension and Z, X Mean temperature Mean value of theIndicative of the temperature friction/random motions

FIG. 8 depicts V's of big data: volume, velocity and variety, accordingto one or more embodiments. The data collected may come from severalfactories. The data may be a high volume data and a solution may need tobe delivered in near real time so data is also a high velocity data. Asdiscussed, the data can be structured, un-structured and audio data, sodata may comprise a wide variety.

Abusive Operation Identification

In one or more embodiments, a motor may need to be operated in aparticular speed range. When the speed range increases, the pressure atwhich the motor is operating also increases. Thus, leading to faults inthe motor.

When there is a fault in a plant, the motor may produce more turbulentvibrations. The more turbulent vibrations may add more harmonics. Due toincreased harmonics, there may be a change in many statisticalproperties of a vibrating motor. When a pump is operated in an abusivemanner, the pump generates random vibrations and thus more harmonics. Inorder to capture the difference in normal operation and abusiveoperation, the set of features discussed in Table 1 may be considered todevelop a machine learning based solution for identification of theabusive operation.

TABLE 2 Algorithm F1 Score Precision Recall Accuracy SVM .55 .68 .49 .64Naïve Bayes .50 .65 .33 .57 Random Forest .59 .61 .58 .62

In an example embodiment, the results in Table 2 may be generated bycollecting data from a 5 hp pump. To generate each record for a trainingfile, the pump may be operated for 30 minutes and then the featuresmentioned in Table 1 may be calculated from the batch of 30 minutes ofdata.

In one or more embodiments, SVM algorithm may provide highest precisionand random forest algorithm may provide a highest recall. In case ofabusive operation, both precision and recall may be important.

FIG. 4 shows a circular gauge depicting circular equipment failure,according to one embodiment. One of the issues of analytics may beeffective visualization of the processed results and/or alarm system. Inone approach, results may be mapped into a simple “Circular Gauge” witha normalized scale of 0-100, where a user may set scales for setting upan alarm and scaling up predictive maintenance issue on rotor drivenequipment. Thus, complex results of Big Data IoT analytics associatedwith rotor driven equipment may be visualized by applying the techniquesdisclosed herein.

The results may be visualized through a web and/or mobile application.The web and/or mobile application may be associated with devices such aspersonal computers, laptops, mobile device, tablets etc.

In one or more embodiments, a retrofit sensor mounting may be used tomonitor rotor driven equipment. The monitoring may be, for example, in afactory with multiple machines. Sensor data may be fed to a cloud servervia a data hub (such as a mobile app). The data hub may collect, analyzeand store the sensor data using Big Data technology (such as Kafka,NoSQL, Cassandra and map reduced systems like Apache Spark).

In one or more embodiments, big data may be a term used to refer tolarge data sets. The data sets may be so large and complex thattraditional data processing systems may be inadequate to handle the datasets. The data collected at IoT sensors associated with rotor drivenequipment may be extremely large and complex. The data may be collectedonto a big data server over a cloud. The big data server may refer todistributed one or more servers associated with the IoT sensors.

In one or more embodiments, data may be collected with primary meta-dataclassification such as “Baseline” and “Test”, where baseline refers tonormal operating condition and/or a condition referred to a healthymachine. Test data may be classified according to the need of testing.Historical statistics like energy consumption of different loads,machines, shifts, etc., may be tracked using an energy efficientmechanism.

In an example embodiment, sensor data may be determined from a sensorsuch as a machine wearable sensor. The sensor may be associated with arotor driven equipment.

In one or more embodiments, a communication network may be one of aWiFi, 2G, 3G, 4G, GPRS, EDGE, Bluetooth, ZigBee, Piconet of BLE, Zwaveor a combination thereof.

In one or more embodiments, the machine learning engine may beassociated with a machine learning algorithm

In an example embodiment, a depiction on a user interface may be acircular gauge type representation as shown in FIG. 4.

Further, the circular gauge may be associated with color schemes such asred, yellow and green. In an exemplary embodiment, the color scheme redmay indicate an alarming rotor driven equipment condition, yellow mayindicate an impending rotor driven equipment issue and green mayindicate a healthy rotor driven equipment.

In one or more embodiments, an alarm may be raised when color scheme isone of a yellow and a red.

In one or more embodiments, a method of predicting a rotor drivenequipment issue may include: collecting one or more rotor drivenequipment readings from one or more IoT sensors through a processor; andtransmitting over a communication network and also sending the one ormore collected rotor driven equipment readings to a machine learningengine. Further, the method may include visualizing one or more rotordriven equipment issues through a processor based on an analysis througha big data engine and indicating the one or more electrical line issuesthrough a user interface dynamic. The user interface dynamic may be apredictive maintenance circular gauge. An alarm may be set, through aprocessor, for the one or more rotor driven equipment issues.

In one or more embodiments, the machine learning engine may beassociated with a machine learning algorithm. The machine learningengine may be capable of receiving rotor driven equipment data from oneor more sensors. The machine learning engine may process the receiveddata to recognize one of a pattern and a deviation to issue alarm andcontrol commands pertaining to the rotor driven equipment in associationwith the communications network.

Further, the machine learning engine may be associated with amulti-classification engine such as an oblique and/or support vectormachine. The support vector machines may be supervised learning modelswith associated learning algorithms that analyze data and recognizepatterns. The supervised learning models may be used for classificationand regression analysis.

In one or more embodiments, steps of the multi-classification engine mayinclude data transformation to achieve maximum separation among faulttypes. The data transformation may lead to more accurate multiclassification e.g. linear discriminant functions. Further, nonlinearhyper plane fitting may be done to classify different fault types, e.g.quadratic hyper planes in transformed variable space. Developing ameasure to represent the degree of fault based on machine learningmulti-fault classification approach. The intensity of fault may becalculated, e.g. posterior probability of fault type. The degree offault information may be mapped onto the circular gauge such as in FIG.4. For example, different fault type posterior probabilities may becombined to get circular gauge representation. User calibration of thecircular gauge may be enabled to include user intuition about themachine state into the analytics process. The multi classification mayend when the user agrees with the circular gauge.

In one or more embodiments, the distributed power line diagnosis systemmay utilize multi-layer big gauge based Big Data visualization tosimplify issues and alarms associated with rotor driven equipment.

The rotor driven equipment diagnosis system may include two layers,first front layer being a gauge (single or multi-parametric ormulti-dimensional) and second layer being analytical. A user may set analarm for rotor driven equipment issues such as oil state, oil level,high belt tension, etc., based on direct rules and/ormulti-classification machine learning algorithm using a Base-Line (BL)calibration method.

FIG. 1 is a diagrammatic representation of an overall architecture faultdetection in rotor driven equipment using low frequency vibration dataand big data architecture, according to one embodiment. The overallarchitecture 100 may include a Zigbee sensor 102, BLE sensor 104 tocollect rotor driven equipment data. The rotor driven equipment data maybe collected onto a mobile data collector 106 associated with aweb-socket module subscriber 108. The web-socket module subscriber 108may be associated with data storage topics module 114 and in turnassociated with a data storage module 122. The data storage module 122may be associated with Cassandra 124. The web-socket module subscriber108 maybe associated with PM (predictive maintenance) topics 1-n 118.The PM topics 1-n 118 may be associated with a Spark analytics batch126. The spark analytics batch 126 may be associated with notificationstopic 120. The notifications topic 120 may be associated with web-socketmodule publisher 112. The web-socket module publisher 112 may beassociated with a mobile view application 110. A kafka module 116 mayact as a point of connecting data collection, analytics and resultpublishing functionalities.

FIG. 2 is a diagrammatic representation of a data processing systemcapable of processing a set of instructions to perform one or more ofthe methodologies herein, according to an example embodiment. FIG. 2shows a diagrammatic representation of machine in an exemplary form of acomputer system 226 within which a set of instructions for causing themachine to perform any one or more of the methodologies discussedherein, may be executed. In various embodiments, the machine operates asa standalone device and/or may be connected (e.g. networked) to othermachines.

In a networked deployment, the machine may operate in the capacity of aserver and/or a client machine in server-client network environment andor as a peer machine in a peer-to-peer (or distributed) networkenvironment. The machine may be a Personal Computer (PC), a tablet PC, aSet-Top Box (STB), a Personal Digital Assistant (PDA), a cellulartelephone, a web appliance, a network router, a switch and/or bridge, anembedded system and/or any machine capable of executing a set ofinstructions (sequential and/or otherwise) that specify actions to betaken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform one and/or more of themethodologies discussed herein.

For example, computer system 226 includes a processor 202 (e.g. acentral processing unit (CPU) or a graphics processing unit (GPU) orboth), a main memory 204 and a static memory 206, which communicate witheach other via a bus 208. The computer system 226 may further include adisplay unit 210 (e.g. a liquid crystal displays (LCD) and/or a cathoderay tube (CRT)). The computer system 226 also includes an alphanumericinput device 212 (e.g. a keyboard), a cursor control device 214 (e.g. amouse), a disk drive unit 216, a signal generation device 218 (e.g. aspeaker) and a network interface device 220.

The disk drive unit 216 includes a machine-readable medium 222 on whichis stored one or more sets of instructions 224 (e.g. software) embodyingany one or more of the methodologies and/or functions described herein.The instructions 224 constituting machine-readable media may also residecompletely or at least partially, within the main memory 204 and/orwithin the processor 202 during execution thereof by the computer system226, the main memory 204 and the processor 202.

The instructions 224 may further be transmitted and/or received over anetwork 200 via the network interface device 220. While themachine-readable medium 222 is shown in an example embodiment to be asingle medium, the term “machine-readable medium” should be taken toinclude a single medium and/or multiple media (e.g. a centralized and/ordistributed database, and/or associated caches and servers) that storethe one or more sets of instructions. The term “machine-readable medium”shall also be taken to include any medium that is capable of storing,encoding and/or carrying a set of instructions for execution by themachine and that cause the machine to perform any one or more of themethodologies of the various embodiments. The term “machine-readablemedium” shall accordingly be taken to include, but not be limited tosolid-state memories, optical and magnetic media, and carrier wavesignals.

FIG. 3 is a process flow diagram detailing the operations of a method ofpredicting an electrical line issue, according to one or moreembodiments. A method of predicting an electrical line issue maycomprise the steps of: (i) collecting one or more rotor driven equipmentreadings from one or more IoT sensors 302; (ii) transmitting thecollected one or more rotor driven equipment readings to a machinelearning engine 304; (iii) visualizing one or more rotor drivenequipment issues based on an analysis through a big data engine 306;(iv) indicating the one or more rotor driven equipment issues through auser interface dynamic 308; and (v) setting an alarm for the one or morerotor driven equipment through one or more of a rule based engine and amulti-classification machine learning engine 310.

FIG. 4 is a diagrammatic representation of a circular gauge to depict astate of a rotor driven equipment, according to one example embodiment.

In an example embodiment, the rotor driven equipment system may be basedon IoT (Internet of Things). The IoT based rotor driven equipment systemmay include sensors such as machine wearable sensors. Further, thesystem may be used for overseeing process control and predictivemaintenance of one or more rotor driven equipment. The system mayinclude a plurality of machine-wearable sensors, each of which may beassociated with a rotor driven equipment. Each sensor is capable oftransmitting captured rotor driven equipment data over a wirelesscommunication network. The system may further include a sensor networkfor receiving and transmitting the captured data over a communicationnetwork and a machine learning algorithm engine capable of receivingdata from the sensor network. The machine learning algorithm engine mayprocess the received data to recognize one of a pattern and a deviationto issue control commands pertaining to the machine. Lastly, the systemmay include one or more control modules disposed in operativecommunication with a local firmware board associated with the rotordriven equipment where the local firmware board is capable of receivingand sending one or more control commands, executing the control commandsand transmitting calculated/computed data over a communication network.

In one or more embodiments, a three stage computation may be necessaryfor rotor driven equipment diagnosis. First computation may be at thelocal firmware board, second computation at the data hub and lastcomputation at the IoT server. A computation engine may be associatedwith one or more of the local firmware board, the data hub and IoTserver over a communication network.

In one or more embodiments, a learning outcome as a result of analysisat IoT server may be dependent on recognition of one of a pattern anddeviation recognized by the machine learning engine.

In an example embodiment, data may be collected from diverse locationssuch as 10,000 factory locations for 3P (prescriptive, preventative andpredictive) maintenance by using a combination of Cassandra (distributeddatabase), Storm and/or Spark real time to process the data in a realtime Big Data architecture using a broker system such as Kafka forstoring the alarms as buffer database and then using Storm and/orCassandra like distributed database for an MRO (Maintenance, Repair andOperation) system. The real time Big Data architecture may be associatedwith the IoT server.

In one or more embodiments, 3P maintenance may be a possibility for arotor driven equipment. Big data methodologies may be employed toanalyze data obtained from various locations through an IoT sensornetwork. Big data may be used to describe a massive volume of bothstructured and unstructured data. Large volumes of data may be difficultto process using a traditional database and traditional softwaretechniques. Therefore, a distributed real-time computation system suchas Apache Storm may be used for distributed rotor driven equipmentdiagnosis.

In an example embodiment, a rotor driven equipment fault detectionsystem may be associated with distributed databases. The rotor drivenequipment fault detection system may be associated with a big datasystem.

In one or more embodiments, one or more rotor driven equipment issuesmay be determined based on one or more computations. Further, thedetermination may be based on an analysis associated with a machinelearning engine.

In one or more embodiments, an alarm may be set through one of a rulebased engine and a multi-classification machine learning engine.

In one or more embodiments, the communication network is one of Wi-Fi,2G, 3G, 4G, GPRS, EDGE, Bluetooth, ZigBee, Piconet of BLE, Zwave or acombination thereof.

In one or more embodiments, an alarm may be raised over thecommunication network, through one of a notification on the mobileapplication, Short Message Service (SMS), email or a combinationthereof.

In one or more embodiments, a system of detecting faults in rotor drivenequipment may use an Internet of Things (IoT) based architecture forround the clock tracking of machines. The system may include sensor onchip technology, wireless network and a computation engine. The systemmay reduce operation costs of the machines by a large percentage.Reduction in costs may be achieved through a combination of singlesilicon chip, open source networking and cloud based software. Further,costs may be reduced by not using Fast Fourier Transforms and reducingsampling rates.

FIG. 9 is a diagrammatic representation of a predictive maintenancecircular gauge associated with a rotor driven equipment, according toone embodiment. The predictive maintenance circular gauge may depictvarious electrical line issues such as rotor driven equipment failure,belt tension, oil state, operation condition, oil level, and vibration.

The predictive maintenance circular gauge may be associated with one ormore color indications. The color indications may include red, yellowand green states. The red may indicate a danger mode of operationwherein the rotor driven equipment may comprise one of the failed rotorand/or the rotor driven equipment which is about to fail. The yellow mayindicate an intermediate state of operation for the rotor drivenequipment that the predictive maintenance gauge is associated with. Thegreen state may indicate an ideal and/or smooth state of operation forthe rotor driven equipment that the predictive maintenance circulargauge is associated with.

In one or more embodiments, an alarm may be raised when color scheme isone of a yellow and a red.

In one or more embodiments, IoT sensors may be enabled to computethrough a computation engine. Computation engine may be associated withcalculation of one or more of a kurtosis, crest factor, and percentile.

In one or more embodiments, a method of detecting faults in rotor drivenequipment may include generating multiple axis vibration data from oneor more vibration sensors communicatively coupled to the rotor drivenequipment and collecting the data from the one or more machine wearablesensors onto a mobile data collector. Further, the method includessampling the data at random to estimate a maximum value and controllinga sampling error under a predefined value. Still further, the methodincludes analyzing the data through a combination of Cartesian toSpherical transformation, statistics of extracted entity of one or morespherical variables, big data analytics engine and a machine learningengine. A fault associated with the rotor driven equipment may bedisplayed on a user interface.

FIG. 10 is a flow diagram depicting a flow of data associated with arotor driven equipment, according to one embodiment. FIG. 10 depictsflow of three axis vibration data onto a fuel gauge based predictivemaintenance view. Three axis vibrational data 1002 may be received. Thethree axis vibration data may be transformed to a rotationally invariantform 1004. Long term statistics may be extracted for r, θ and φ 1006. Aroot cause may be discovered in association with a machine learningalgorithm 1008. For example, bad oil, bearing failure etc. The data maybe classified based on a health state 1010. The health state of rotordriven equipment may be classified into color schemes such as red,yellow and green. In an exemplary embodiment, the color scheme red mayindicate an alarming rotor driven equipment condition, yellow mayindicate an impending rotor driven equipment issue and green mayindicate a healthy rotor driven equipment.

In one or more embodiments, an alarm may be raised when color scheme isone of a yellow and a red.

Although the present embodiments have been described with a reference tospecific example embodiments, it will be evident that variousmodifications and changes may be made to these embodiments withoutdeparting from the broader spirit and scope of the various embodiments.For example, the various devices and modules described herein may beenabled and operated using hardware circuitry, firmware, software or anycombination of hardware, firmware, and software (e.g., embodied in amachine readable medium). For example, the various electrical structureand methods may be embodied using transistors, logic gates andelectrical circuits (e.g. application specific integrated (ASIC)circuitry and/or in Digital Signal Processor (DSP) circuitry).

In addition, it will be appreciated that the various operations,processes and methods disclosed herein may be embodied in amachine-readable medium and/or a machine accessible medium compatiblewith a data processing system (e.g. a computer devices) and may beperformed in any order (e.g. including using means for achieving thevarious operations). The medium may be, for example, a memory, atransportable medium such as a CD, a DVD, a Blu-Ray™ disc, a floppydisk, or a diskette. A computer program embodying the aspects of theexemplary embodiments may be loaded onto a retail portal. The computerprogram is not limited to specific embodiments discussed above and may,for example, be implemented in an operating system, an applicationprogram, a foreground or background process, a driver, a network stackor any combination thereof. The computer program may be executed on asingle computer processor or multiple computer processors.

Accordingly, the specifications and drawings are to be regarded in anillustrative rather than a restrictive sense.

What is claimed is:
 1. A method of detecting faults in a rotor drivenequipment comprising: generating multiple axis vibration data from oneor more vibration sensors communicatively coupled to the rotor drivenequipment; collecting the data from the one or more machine wearablesensors onto a mobile data collector; sampling, through a processor, thedata at random to estimate a maximum value; controlling a sampling errorunder a predefined value, wherein the sampling error is associated withthe data; analyzing the data through a combination of Cartesian toSpherical transformation, statistics of extracted entity of one or morespherical variables, big data analytics engine and a machine learningengine, wherein the Cartesian to spherical transformation is to makevibrational vectors invariant; and displaying on a user interface afault associated with the rotor driven equipment.
 2. The method of claim1, further comprising determining the at least one rotor drivenequipment issue based on one or more computations.
 3. The method ofclaim 2, wherein a computation engine enables the one or morecomputations including at least one of a series of entity extraction ofvibrational data, RMS, variance and kurtosis of azimuthal angle, peak toRMS ratio, percentiles ratio, ratio of variance of each individualvibration axis.
 4. The method of claim 1, wherein the alarm is setthrough at least one of a rule based engine and a multi-classificationmachine learning engine.
 5. The method of claim 1, wherein the userinterface dynamic is a predictive maintenance circular gauge.
 6. Themethod of claim 1, wherein the rotor driven equipment issues include atleast one of a belt tension, filter condition, abusive operation, oillevel, and viscosity of oil; wherein the issues are discovered through amachine learning multi-classification; and wherein the machine learningmulti-classification includes at least one of a neural network, randomforest, logistical regression, and support vector machine (SVM).
 7. Themethod of claim 1 wherein the communication network is one of Wi-Fi, 2G,3G, 4G, GPRS, EDGE, Bluetooth, ZigBee, Piconet of BLE, Zwave or acombination thereof.
 8. The method of claim 1, wherein the alarm israised over the communication network through one of a notification onthe mobile application, Short Message Service (SMS), email or acombination thereof.
 9. A method of predicting rotor driven equipmentissues, the method comprising: collecting, through a processor, dataassociated with at least one machine wearable sensor associated with arotor driven equipment; transmitting the data collected at the at leastone machine wearable sensor over a communication network to a mobiledata collector, wherein the data collected is over a finite time periodand transmitted to a machine learning engine, and wherein the machinelearning engine is associated with a computer database hosting real timeand historical data; visualizing, through a processor, at least onerotor driven equipment issue based on an analysis through a combinationof a big data engine and a machine learning engine; indicating the atleast one rotor driven equipment issue through a user interface dynamic;and setting an alarm, through a processor, for the at least one rotordriven equipment issue.
 10. The method of claim 9, further comprising ofdetermining the at least one rotor driven equipment issue based on oneor more computations.
 11. The method of claim 10, wherein a computationengine enables the one or more computations.
 12. The method of claim 9,wherein the alarm is set through at least one of a rule based engine anda multi-classification machine learning engine.
 13. The method of claim9, wherein the user interface dynamic is a predictive maintenancecircular gauge.
 14. The method of claim 9, wherein the rotor drivenequipment issues include at least one of a belt tension, abusiveoperation, oil level, and oil state.
 15. The method of claim 9, whereinthe communication network is one of Wi-Fi, 2G, 3G, 4G, GPRS, EDGE,Bluetooth, ZigBee, Piconet of BLE, Zwave or a combination thereof. 16.The method of claim 9, wherein the alarm is raised over thecommunication network through one of a notification on the mobileapplication, Short Message Service (SMS), email or a combinationthereof.